Intelligent remote sensing data analyis by MTA SZTAKI and INRIA SAM

Building
change detection

This page introduces a joint work of the DEVA Laboratoy of MTA SZTAKI and the Ariana Project Term of INRIA Sophia-Antipolis, France, on building extraction and change detection in multitemporal aerial and satellite images (2008-2011). For more information please contact Csaba Benedek.

Abstract

In
this work we have introduced a new probabilistic method
which integrates building extraction with change detection
in remotely sensed image pairs. A global optimization
process attempts to find the optimal configuration of
buildings, considering the observed data, prior knowledge,
and interactions between the neighboring building parts.
We present methodological contributions in three key issues:
(1) We have implemented a novel object-change modeling
approach based on Multitemporal Marked Point Processes,
which simultaneously exploits low level change information
between the time layers and object level building description
to recognize and separate changed and unaltered buildings.
(2) To answering the challenges of data heterogeneity
in aerial and satellite image repositories, we have constructed
a flexible hierarchical framework which can create various
building appearance models from different elementary feature
based modules. (3) To simultaneously ensure the convergence,
optimality and computation complexity constraints raised
by the increased data quantity, we have adopted the quick
Multiple Birth and Death optimization technique for change
detection purposes, and propose a novel non-uniform stochastic
object birth process, which generates relevant objects
with higher probability based on low-level image features.

Outline

The aim
of this work is to propose an automatic method which attempts
to identify buildings and building changes in multi-temporal
aerial and satellite images, with minimized user interaction.

The
brief specficiation of the progam is the following:

Input:
pair of co-registered grayscale or color images, taken at
different time instances

Output:
in each image we provide the size, position and orientation
parameters of the detected houses/house parts, with giving information
which objects are new, demolished, modified and unchanged

Feature
selection

In
the proposed model, low level and object level features are distinguished.
Low level descriptors are extracted around each pixel such as
typical color or texture, and local similarity between the time
layers. They are used by the exploration process to estimate where
the buildings can be located, and how they can look like: the
birth step generates objects in the estimated built-up regions
with higher probability. On the other hand, object level features
characterize a given object candidate u, exploited for the fitness
calculation of the proposed oriented rectangles. Building verification
is primarily based on the object level features thus their accuracy
is crucial (See Fig. 1).

Fig. 1. Feature maps of an image from the COTE
D'AZUR test set.

Marked
Point Process Model

The
Marked Point Process framework enables to characterize whole populations
instead of individual objects, through exploiting information
from entity interactions. Following an inverse approach one should
assign a fitness value to each possible object configuration and
an optimization process attempts to find the configuration with
the highest confidence. In this way, flexible object appearance
models can be adopted, and it is also straightforward to incorporate
prior shape information and object interactions. According to
the classical Markovian discipline, each object may only affect
its neighbours directly. This property limits the number of interactions
in the population and results in a compact description of the
global scene, which can be analyzed efficiently. To realize the
Markov-property, one should define first a ~ neighborhood relation
between the objects. As for optimization, we apply the Multiple
Birth and Death technique, which evolves the population of buildings
by alternating object proposition (birth) and removal (death)
steps in a simulated annealing framework.

For
a general introduction and other applications of Marked Point
Processes visit the demo
site of the Ariana project.

Experiments

We
have evaluated our method using eight significantly different
data sets whose main properties. Four image collections contain
multitemporal aerial or satellite photos from the monitored regions,
which enables testing both the building extraction and the change
detection abilities of the proposed mMPP model. The remaining
four data sets contain standalone satellite images acquired from
Google Earth, which are only exploited in the evaluation of the
building appearance model. To guarantee the heterogeneity of the
test sets, we have chosen completely different geographical regions
as listed in Table 1. We collected samples from densely populated
suburban areas, and built a manually annotated database for validation.
For parameter settings, we have chosen in each data set 2-8 buildings
as training data, while the remaining Ground Truth labels have
only been used to validate the detection results. Some qualitative
results are shown in Fig. 2.